Synthesis of dimethyl carbonate by transesterification of propylene carbonate with methanol on ceo2-la2o3 oxides prepared by the soft template method

In this study, CeO2, La2O3, and CeO2-La2O3 mixed oxide catalysts with different Ce/La molar ratios were prepared by the soft template method and characterized by different techniques, including inductively coupled plasma atomic emission spectrometry, X-ray diffraction, N2 physisorption, thermogravimetric analysis, and Raman and Fourier transform infrared spectroscopies. NH3 and CO2 adsorption microcalorimetry was also used for assessing the acid and base surface properties, respectively. The behavior of the oxides as catalysts for the dimethyl carbonate synthesis by the transesterification of propylene carbonate with methanol, at 160 °C under autogenic pressure, was studied in a stainless-steel batch reactor. The activity of the catalysts was found to increase with an increase in the basic sites density. The formation of dimethyl carbonate was favored on medium-strength and weak basic sites, while it underwent decomposition on the strong ones. Several parasitic reactions occurred during the transformation of propylene carbonate, depending on the basic and acidic features of the catalysts. A reaction pathway has been proposed on the basis of the components identified in the reaction mixture

The Small Ruminant Nutrition System: development and evaluation of a goat submodel

The Small Ruminant Nutrition System (SRNS) is a computer model based on the structure of the Cornell Net Carbohydrate and Protein System for Sheep.A version of the SRNS for goats is under development and evaluation. In the SRNS for goats, energy and protein requirements are predicted based on the equations developed for the SRNS for sheep, modified to account for specific requirements of goats. Feed biological values are predicted based on carbohydrate and protein fractions and their ruminal degradation rates, on forage, concentrate and liquid passage rates, on microbial growth, and on physically effective fiber. The evaluation of the SRNS for goats based on literature data showed that the SRNS accurately predicted the ADG of kids (RMSEP = 32.5 g/d; r2 = 0.85; CCC = 0.91), and the daily MEI (RMSEP = 0.24 Mcal/d g/d; r2 = 0.99; CCC = 0.99) and the energy balance (RMSEP = 0.20 Mcal/d g/d; r2 = 0.87; CCC = 0.90) of goats

A New Point of View on Skin-Friction Contributions in Adverse-Pressure-gradient Turbulent Boundary Layers

Skin-friction decompositions such as the so-called FIK identity (Fukagata et al., 2002) are useful tools in identifying relevant contributions to the friction, but may also lead to results difficult to interpret when the total friction is recovered from cancellation of multiple terms with large values. We propose a new formulation of the FIK contributions related to streamwise inhomogeneity, which is derived from the convective form of the momentum equation and using the concept of dynamic pressure. We examine turbulent boundary layers subjected to various pressure-gradient conditions, including cases with drag-reducing control. The new formulation distinguishes more precisely the roles of the free-stream pressure distribution, wall-normal convection, and turbulent fluctuations. Our results allow to identify different regimes in adverse-pressure-gradient turbulent boundary layers, corresponding to different proportions of the various contributions, and suggest a possible direction towards studying the onset of mean separation

Automatic Adaptation of SOA Systems Supported by Machine Learning

Part 3: Service OrientationInternational audienceRecent advances in the development of information systems have led to increased complexity and cost in terms of the required maintenance and management. On the other hand, systems built in accordance with modern architectural paradigms, such as Service Oriented Architecture (SOA), posses features enabling extensive adaptation, not present in traditional systems. Automatic adaptation mechanisms can be used to facilitate system management. The goal of this work is to show that automatic adaptation can be effectively implemented in SOA systems using machine learning algorithms. The presented concept relies on a combination of clustering and reinforcement learning algorithms. The paper discusses assumptions which are necessary to apply machine learning algorithms to automatic adaptation of SOA systems, and presents a machine learning-based management framework prototype. Possible benefits and disadvantages of the presented approach are discussed and the approach itself is validated with a representative case study

Drag Assessment for Boundary Layer Control Schemes with Mass Injection

The present study considers uniform blowing in turbulent boundary layers as active flow control scheme for drag reduction on airfoils. The focus lies on the important question of how to quantify the drag reduction potential of this control scheme correctly. It is demonstrated that mass injection causes the body drag (the drag resulting from the stresses on the body) to differ from the wake survey drag (the momentum deficit in the wake of an airfoil), which is classically used in experiments as a surrogate for the former. This difference is related to the boundary layer control (BLC) penalty, an unavoidable drag portion which reflects the effort of a mass-injecting boundary layer control scheme. This is independent of how the control is implemented. With an integral momentum budget, we show that for the present control scheme, the wake survey drag contains the BLC penalty and is thus a measure for the inclusive drag of the airfoil, i.e. the one required to determine net drag reduction. The concept of the inclusive drag is extended also to boundary layers using the von Karman equation. This means that with mass injection the friction drag only is not sufficient to assess drag reduction also in canonical flows. Large Eddy Simulations and Reynolds-averaged Navier-Stokes simulations of the flow around airfoils are utilized to demonstrate the significance of this distinction for the scheme of uniform blowing. When the inclusive drag is properly accounted for, control scenarios previously considered to yield drag reduction actually show drag increase

Advancements in combining electronic animal identification and augmented reality technologies in digital livestock farming

Modern livestock farm technologies allow operators to have access to a multitude of data thanks to the high number of mobile and fixed sensors available on both the livestock farming machinery and the animals. These data can be consulted via PC, tablet, and smartphone, which must be handheld by the operators, leading to an increase in the time needed for on-field activities. In this scenario, the use of augmented reality smart glasses could allow the visualization of data directly in the field, providing for a hands-free environment for the operator to work. Nevertheless, to visualize specific animal information, a connection between the augmented reality smart glasses and electronic animal identification is needed. Therefore, the main objective of this study was to develop and test a wearable framework, called SmartGlove that is able to link RFID animal tags and augmented reality smart glasses via a Bluetooth connection, allowing the visualization of specific animal data directly in the field. Moreover, another objective of the study was to compare different levels of augmented reality technologies (assisted reality vs. mixed reality) to assess the most suitable solution for livestock management scenarios. For this reason, the developed framework and the related augmented reality smart glasses applications were tested in the laboratory and in the field. Furthermore, the stakeholders’ point of view was analyzed using two standard questionnaires, the NASA-Task Load Index and the IBM-Post Study System Usability Questionnaire. The outcomes of the laboratory tests underlined promising results regarding the operating performances of the developed framework, showing no significant differences if compared to a commercial RFID reader. During the on-field trial, all the tested systems were capable of performing the task in a short time frame. Furthermore, the operators underlined the advantages of using the SmartGlove system coupled with the augmented reality smart glasses for the direct on-field visualization of animal data

Soft-templated NiO–CeO2 mixed oxides for biogas upgrading by direct CO2 methanation

The catalytic performance in the direct CO2 methanation of a model biogas is investigated on NiO-CeO2 nanostructured mixed oxides synthesized by the soft-template procedure with different Ni/Ce molar ratios. The samples are thoroughly characterized by means of ICP-AES, XRD, TEM and HR-TEM, N2 physisorption at -196 °C, and H2-TPR. They result to be constituted of CeO2 rounded nanocrystals and of polycrystalline needle-like NiO particles. After a H2-treatment at 400 C for 1 h, the surface basic properties and the metal surface area are also assessed using CO2 adsorption microcalorimetry and H2-pulse chemisorption measurements, respectively. At increasing Ni content the Ni0 surface area increases, while the opposite occurs for the number of basic sites. Using a CO2/CH4/H2 feed, at 11,000 cm3 h-1 gcat-1, CO2 conversions in the 83-89 mol% range and methane selectivities >99.5 mol% are reached at 275 °C and atmospheric pressure, highlighting the very good performances of the investigated catalysts

Storytelling integration of the internet of things into business processes

© Springer Nature Switzerland AG 2018. This paper discusses the integration of Internet of Things (IoT) into Business Processes (BPs). To define the business logic of thing-aware BPs, existing approaches extend traditional workflow languages (i.e., who does what, why, when, and where) with constructs like things’ roles. However, this way of defining the business logic restricts things’ operations and, thus, hinders them from initiating ad-hoc/opportunistic collaboration with peers. To overcome this limitation, we tap into the storytelling principles to introduce the concept of Process of Things (PoT) as a new way of integrating IoT into BPs. A PoT is specified as a story whose script indicates the characters that things will play as well as the scenes that will feature these things. A PoT, also, allows things to collaborate by offering value-added services to end-users. For demonstration purposes, a hospital scenario is implemented using a combination of real and simulated sensors along with different IoT technologies and communication protocols